Translation of digital phenotypic traits: from image to commercial relevance
Dr. Shital Dixit PAG XXI 15th Jan 2013
Presentation outline • Challenges in plant phenotyping • Facility of PhenoFab®
• Novelty of PhenoFab® projects • Case study: Correlating early digital traits to later yield related traits • Conclusions
Shital Dixit
15-01-2013
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Challenges in plant phenotyping Cold
Salt
Drought
• • •
Objective phenotyping Digital Phenotyping High-throughput
Abiotic agents
Biotic agents
Subjective/bias scoring Shital Dixit
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Digital Phenotyping
Objective data collection from digital images to measure morphological and physiological characteristics through image analysis is known as digital phenotyping
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PhenoFab® – the facility
3D Conveyor Scanalyzer
•Greenhouse setup with climate control •Moving pots/trays: capacity: 1040 pots
•GMO authorized Shital Dixit
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3D Image stations Capacity: Plant Height 2.6 mts.
Visible light -Shape - Color
NIR - internal structure - Water content
Fluorescence -Chlorophyll analysis
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Novelty of PhenoFab facility
Agri Food Biotech company
Automation of scientific images
Software/hardware development
Value creation for Customer
Traits/genes/genomics/bioinformatic/data analysis
Shital Dixit
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Drought, WUE Cotton Tobacco
NUE Root phenotyping Flowering time Crop architechture Height, width Canopy structure Heavy metal toxicity Effect of growth substrate Crop growth rate Cabbage Fruit shape, color and Taraxacum shelf life
Tomato-roots
Pepper
Sorghum
Maize
Miscanthus
Canola
Rice
Ficus
Sugarbeet
Grass
Wheat
Tomato
Cabbage
Shital Dixit
Cucumber
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Root imaging using PhenoFab
• Shoot/root ratio • Effects of stress Drought Salt Heat nutrition
Digital detection of roots by the algorithm
Slide 9
Root imaging using PhenoFab Direct root mass measurement
PhenoFab root measurement
93 % Correlation !
PhenoFab root measurement
Direct root mass measurement 93 % Correlation !
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PhenoFab 速 project workflow
Idea
PhenoFab 速
to workplan
5 weeks
From image to digital phenotyping
Statistical correlation study
Conclusions
4 months Shital Dixit
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PhenoFab project deliverables • Pixel count RGB • Growth curves • Growth model • Project report • Full database • 4 sides + top growth movies • RGB • NIR • Fluo • Overall impression images Shital Dixit
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c
Project report
Poster no. PO165
KeyGene booth 111 Shital Dixit
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Role of digital phenotypes in predicting later yield related traits in crops
From image to digital phenotyping
Later crop trait
Early crop trait Shital Dixit
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Case study: Correlating early digital traits to later yield traits in tomato Experimental Set-up PhenoFab 30 genotypes 5 replicates 5 weeks
Breeders/commercial greenhouse 30 genotypes Two greenhouse locations
Early crop trait
Later crop trait Shital Dixit
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Case study: Correlating early digital traits to later yield traits in tomato Early crop trait Digital phenotype data •Breeding values •Daily averages •Growth parameters
Later crop trait
Later “yield-related” traits •Fruit quantity •Fruit yield (kg)
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Correlation study using single digital phenotype
• Yield related trait:
Tomato fruit weight
0.4 R-square (-0.63 correlation)
• Single digital phenotype describing plant architecture
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Correlation study using single digital phenotype
Digital phenotype
0.4 R-square (-0.63 correlation)
Field tomato fruit weight Shital Dixit
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Correlation study using two digital phenotype • Yield related trait:
Tomato fruit weight
0.57 adj. R square (0.75 multiple correlation)
• Model using two digital phenotypes (describing plant architecture)
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Correlation study using two digital phenotype
Digital phenotype model
0.57 adj. R square (0.75 multiple correlation)
Field tomato fruit weight Shital Dixit of 15-01-2013 20 GmbH PhenoFab速 is a registered trademark Keygene N.V. and Lemnatec
Conclusions PhenoFab provides HTP, non-destructive shoot and root measurements. PhenoFab has overcomed data anlaysis challenges for many traits PhenoFab has succesfully demonstrated example of correlating early digital traits to yield in tomato generating commercial relevance for its client.
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The KeyBox system
Idea
KeyBox data
to workplan
From image to digital phenotyping
Statistic correlation study
Conclusions
KeyGene booth 111 Shital Dixit
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Disease phenotyping Susceptibility Score 25.00%
Percentage
20.00%
15.00%
10.00%
5.00%
0.00% Wt WT
S+mlo
A
D+mlo
B
D
C
T+mlo
D
T
E
Arabidopsis
T
D
E
Resistance group
C B
D
D+mlo S+mlo
WT A
Arabidopsis
T+mlo
Wt 0
2
4
6
8
Fold resistance com pared to Wt
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The KeyBoxTM system Fruit quality traits
red area in pixels: 155275 Cracked area in pixels: 3188
red area in pixels: 191589 Cracked area in pixels: 345
red area in pixels: 157381 Cracked area in pixels: 118
red area in pixels: 113847 Cracked area in pixels: 6670
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The KeyBoxTM system Fruit quality traits
Crack: 6892 pixels; 37% Dark spots: 2430 Roundness Deviation: 296
Crack: 84561 pixels;51% Dark spots 1461 Roundness deviation: 25 15-01-2013
The KeyBoxTM system Leaf shape, area, color
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Acknowledgements
Gert-Jan Speckmann Jose Guerra Koen Huvenaars Marco van Schriek Anker Sørensen Shital Dixit
Martijn van Stee
Dirk Vandenhirtz Joerg Vandenhirtz Kevin Nagel Ralph Schunk Shital Dixit
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PhenoFab: The plant phenotyping facility in Europe
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The PhenoFab速 facility
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